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1. Assignment #0: Introductory Concepts of DMDW
Instructor: Dr. Rajkumar
1.1 What is data mining? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation or application of technology developed from databases,
statistics, machine learning, and pattern recognition?
(c) We have presented a view that data mining is the result of the evolution of database
technology. Do you think that data mining is also the result of the evolution of
machine learning research? Can you present such views based on the historical
progress of this discipline? Address the same for the fields of statistics and pattern
recognition.
(d) Describe the steps involved in data mining when viewed as a process of knowledge
discovery.
1.2 How is a data warehouse different from a database? How are they similar?
1.3 Define each of the following data mining functionalities: characterization, discrimination,
association and correlation analysis, classification, regression, clustering, and outlier analysis. Give examples of
each data mining functionality, using a real-life database that you are familiar with.
1.4 Present an example where data mining is crucial to the success of a business. What data
mining functionalities does this business need (e.g., think of the kinds of patterns that
could be mined)? Can such patterns be generated alternatively by data query processing
or simple statistical analysis?
1.5 Explain the difference and similarity between discrimination and classification, between
characterization and clustering, and between classification and regression.
1.6 Based on your observations, describe another possible kind of knowledge that needs to
be discovered by data mining methods but has not been listed in this chapter. Does it
require a mining methodology that is quite different from those outlined in this chapter?
1.7 Outliers are often discarded as noise. However, one person’s garbage could be another’s
treasure. For example, exceptions in credit card transactions can help us detect the
fraudulent use of credit cards. Using fraudulence detection as an example, propose two
methods that can be used to detect outliers and discuss which one is more reliable.
1.8 Describe three challenges to data mining regarding data mining methodology and user
interaction issues.
1.9 What are the major challenges of mining a huge amount of data (e.g., billions of tuples)
in comparison with mining a small amount of data (e.g., data set of a few hundred
tuple)?
1.10 Outline the major research challenges of data mining in one specific application domain,
such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics.